Main model
# {.tabset}
res_mod_i <- metafor::rma.mv(logOR ~ Cut_off_date, V = seOR^2, random = ~ 1 | study_ID/es_id, data = dat_main)
res_mod_i
##
## Multivariate Meta-Analysis Model (k = 54; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0035 0.0592 51 no study_ID
## sigma^2.2 0.0000 0.0000 54 no study_ID/es_id
##
## Test for Residual Heterogeneity:
## QE(df = 52) = 77.1822, p-val = 0.0133
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6922, p-val = 0.4054
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0273 0.0177 1.5433 0.1228 -0.0074 0.0620
## Cut_off_dateProbable -0.0301 0.0362 -0.8320 0.4054 -0.1012 0.0409
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
res_mod <- metafor::rma.mv(logOR ~ Cut_off_date - 1, V = seOR^2, random = ~ 1 | study_ID/es_id, data = dat_main)
res_mod
##
## Multivariate Meta-Analysis Model (k = 54; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0035 0.0592 51 no study_ID
## sigma^2.2 0.0000 0.0000 54 no study_ID/es_id
##
## Test for Residual Heterogeneity:
## QE(df = 52) = 77.1822, p-val = 0.0133
##
## Test of Moderators (coefficients 1:2):
## QM(df = 2) = 2.3897, p-val = 0.3027
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## Cut_off_dateOfficial 0.0273 0.0177 1.5433 0.1228 -0.0074 0.0620
## Cut_off_dateProbable -0.0028 0.0316 -0.0889 0.9292 -0.0648 0.0591
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1